Pretrained nnU-Net Model from the cMRI M&Ms Challenge 2020

Pretrained model of the winning contribution of the "Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms)".

The model is build with the nnU-Net framework and comprises an ensemble of five 2d and five 3d models.

The model can be downloaded (after setting up the nnU-Net framework) using following command

nnUNet_download_pretrained_model Task114_heart_mnms

Data needs to be available as 3D .nii.gz files. For further specification how to prepare your data see or follow this nnU-Net example on prostate MRI data

Once your data is prepared you can run inference with

# run prediction with 2d nnU-Net
nnUNet_predict -i <path_to_input_folder> -o <path_to_temporary_output_folder_2d> --save_npz -t 114 -m 2d -tr nnUNetTrainerV2_MMS

# run prediction with 3d nnU-Net
nnUNet_predict -i <path_to_input_folder> -o <path_to_temporary_output_folder_3d> --save_npz -t 114 -m 3d_fullres -tr nnUNetTrainerV2_MMS

# ensemble 2d and 3d predictions
nnUNet_ensemble -f <path_to_temporary_output_folder_2d> <path_to_temporary_output_folder_3d> -o <path_to_final_predictions>

Read the original work at

Full P.M., Isensee F., Jäger P.F., Maier-Hein K. (2021) Studying Robustness of Semantic Segmentation Under Domain Shift in Cardiac MRI. In: Puyol Anton E. et al. (eds) Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science, vol 12592. Springer, Cham. https://doi.org/10.1007/978-3-030-68107-4_24